Are you planning to move to Data Engineering?
Are you an ETL dev wanting to be a DE?
Are you feeling overwhelmed?
Data Engineering is a vast field and a multidisciplinary area.
Basic Linux commands to advanced ETL tools and complex Data Modelling Data Engineering involves a lot of learning.
If you have seen the 2022 state of Data Engineering toolset image, chances are you are even more confused.
Airflow, DBT, Snowflake, Fivetran, Synapse, BigQuery, Databricks, Azure Data factory, AWS Athena, Glue, and Redshift are just a few to name and are growing by the day.
While all this may feel daunting, it isn't.
All senior Data Engineers will tell you :
"Learn the fundamentals well. You can move from one tool to the other without much hassle."
If you understand the core concepts, You will be able to design scalable, resilient, and performant data systems.
There is a lot of hype around Data Engineering. Different vendors are promoting their toolsets.
To see beyond the hype, I started to explore the root concepts and best practices of Data Engineering.
These concepts and best practices are as relevant today as they were 20 years back.
- Over the next 12 weeks, I will share what I have learned.
- I will share best practices learned in 12 years
- I will share how I became a Data Engineer
- How you can become a Data Engineer
Sign up for Data Engineering: Beyond The Hype Newsletter.
Every week I will delve deeper into Data Engineering core concepts.
You will receive a weekly email with a detailed understanding of one concept.
0 spams, 0 Unrelated Promotion. Promise!
You will receive weekly email with detailed understanding on one Data Engineering concept.